Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflec...

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Bibliographic Details
Published in:Regional environmental change 2008-12, Vol.8 (4), p.197-205
Main Authors: Eierdanz, Frank, Alcamo, Joseph, Acosta-Michlik, Lilibeth, Krömker, Dörthe, Tänzler, Dennis
Format: Article
Language:English
Subjects:
Climate Change
Climate Change/Climate Change Impacts
Drought
Earth and Environmental Science
Environment
Environmental impact
Environmental policy
Fuzzy logic
Geography
Nature Conservation
Oceanography
Original Article
Regional/Spatial Science
Theory
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Summary:This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.
ISSN:1436-3798
1436-378X
DOI:10.1007/s10113-008-0069-1